{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter Optimization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep-learning models are the state-of-the-art in time series forecasting. They have outperformed statistical and tree-based approaches in recent large-scale competitions, such as the M series, and are being increasingly adopted in industry. However, their performance is greatly affected by the choice of hyperparameters. Selecting the optimal configuration, a process called hyperparameter tuning, is essential to achieve the best performance.\n",
    "\n",
    "The main steps of hyperparameter tuning are:\n",
    "\n",
    " 1. Define training and validation sets.\n",
    " 2. Define search space.\n",
    " 3. Sample configurations with a search algorithm, train models, and evaluate them on the validation set.\n",
    " 4. Select and store the best model.\n",
    "\n",
    "With `Neuralforecast`, we automatize and simplify the hyperparameter tuning process with the `Auto` models. Every model in the library has an `Auto` version (for example, `AutoNHITS`, `AutoTFT`) which can perform automatic hyperparameter selection on default or user-defined search space.\n",
    "\n",
    "The `Auto` models can be used with two backends: Ray's `Tune` library and `Optuna`, with a user-friendly and simplified API, with most of their capabilities.\n",
    "\n",
    "In this tutorial, we show in detail how to instantiate and train an `AutoNHITS` model with a custom search space with both `Tune` and `Optuna` backends, install and use `HYPEROPT` search algorithm, and use the model with optimal hyperparameters to forecast."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Automatic_Hyperparameter_Tuning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install `Neuralforecast`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# !pip install neuralforecast hyperopt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load Data\n",
    "\n",
    "In this example we will use the `AirPasengers`, a popular dataset with monthly airline passengers in the US from 1949 to 1960. Load the data, available at our `utils` methods in the required format. See https://nixtla.github.io/neuralforecast/examples/data_format.html for more details on the data input format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-01-31</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-02-28</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-03-31</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-04-30</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-05-31</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds      y\n",
       "0        1.0 1949-01-31  112.0\n",
       "1        1.0 1949-02-28  118.0\n",
       "2        1.0 1949-03-31  132.0\n",
       "3        1.0 1949-04-30  129.0\n",
       "4        1.0 1949-05-31  121.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuralforecast.utils import AirPassengersDF\n",
    "\n",
    "Y_df = AirPassengersDF\n",
    "Y_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Ray's `Tune` backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we show how to use the `Tune` backend. This backend is based on Ray's `Tune` library, which is a scalable framework for hyperparameter tuning. It is a popular library in the machine learning community, and it is used by many companies and research labs. If you plan to use the `Optuna` backend, you can skip this section."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a Define hyperparameter grid"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `Auto` model contains a default search space that was extensively tested on multiple large-scale datasets. Search spaces are specified with dictionaries, where keys corresponds to the model's hyperparameter and the value is a `Tune` function to specify how the hyperparameter will be sampled. For example, use `randint` to sample integers uniformly, and `choice` to sample values of a list. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a.1 Default hyperparameter grid\n",
    "\n",
    "The default search space dictionary can be accessed through the `get_default_config` function of the `Auto` model. This is useful if you wish to use the default parameter configuration but want to change one or more hyperparameter spaces without changing the other default values.\n",
    "\n",
    "To extract the default config, you need to define:\n",
    "* `h`: forecasting horizon.\n",
    "* `backend`: backend to use.\n",
    "* `n_series`: Optional, the number of unique time series, required only for Multivariate models. \n",
    "\n",
    "In this example, we will use `h=12` and we use `ray` as backend. We will use the default hyperparameter space but only change `random_seed` range and `n_pool_kernel_size`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "from neuralforecast.auto import AutoNHITS\n",
    "\n",
    "nhits_config = AutoNHITS.get_default_config(h = 12, backend=\"ray\")                      # Extract the default hyperparameter settings\n",
    "nhits_config[\"random_seed\"] = tune.randint(1, 10)                                       # Random seed\n",
    "nhits_config[\"n_pool_kernel_size\"] = tune.choice([[2, 2, 2], [16, 8, 1]])               # MaxPool's Kernelsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a.2 Custom hyperparameter grid\n",
    "\n",
    "More generally, users can define fully customized search spaces tailored for particular datasets and tasks, by fully specifying a hyperparameter search space dictionary.\n",
    "\n",
    "In the following example we are optimizing the `learning_rate` and two `NHITS` specific hyperparameters: `n_pool_kernel_size` and `n_freq_downsample`. Additionaly, we use the search space to modify default hyperparameters, such as `max_steps` and `val_check_steps`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhits_config = {\n",
    "       \"max_steps\": 100,                                                         # Number of SGD steps\n",
    "       \"input_size\": 24,                                                         # Size of input window\n",
    "       \"learning_rate\": tune.loguniform(1e-5, 1e-1),                             # Initial Learning rate\n",
    "       \"n_pool_kernel_size\": tune.choice([[2, 2, 2], [16, 8, 1]]),               # MaxPool's Kernelsize\n",
    "       \"n_freq_downsample\": tune.choice([[168, 24, 1], [24, 12, 1], [1, 1, 1]]), # Interpolation expressivity ratios\n",
    "       \"val_check_steps\": 50,                                                    # Compute validation every 50 steps\n",
    "       \"random_seed\": tune.randint(1, 10),                                       # Random seed\n",
    "    }"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Configuration dictionaries are not interchangeable between models since they have different hyperparameters. Refer to https://nixtla.github.io/neuralforecast/models.html for a complete list of each model's hyperparameters.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.b Instantiate `Auto` model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate an `Auto` model you need to define:\n",
    "\n",
    "* `h`: forecasting horizon.\n",
    "* `loss`: training and validation loss from `neuralforecast.losses.pytorch`.\n",
    "* `config`: hyperparameter search space. If `None`, the `Auto` class will use a pre-defined suggested hyperparameter space. \n",
    "* `search_alg`: search algorithm (from `tune.search`), default is random search. Refer to https://docs.ray.io/en/latest/tune/api_docs/suggestion.html for more information on the different search algorithm options.\n",
    "* `backend`: backend to use, default is `ray`. If `optuna`, the `Auto` class will use the `Optuna` backend.\n",
    "* `num_samples`: number of configurations explored."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we set horizon `h` as 12, use the `MAE` loss for training and validation, and use the `HYPEROPT` search algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray.tune.search.hyperopt import HyperOptSearch\n",
    "from neuralforecast.losses.pytorch import MAE\n",
    "from neuralforecast.auto import AutoNHITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoNHITS(h=12,\n",
    "                  loss=MAE(),\n",
    "                  config=nhits_config,\n",
    "                  search_alg=HyperOptSearch(),\n",
    "                  backend='ray',\n",
    "                  num_samples=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "The number of samples, `num_samples`, is a crucial parameter! Larger values will usually produce better results as we explore more configurations in the search space, but it will increase training times. Larger search spaces will usually require more samples. As a general rule, we recommend setting `num_samples` higher than 20. We set 10 in this example for demonstration purposes.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.c Train model and predict with `Core` class"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use the `Neuralforecast` class to train the `Auto` model. In this step, `Auto` models will automatically perform hyperparamter tuning training multiple models with different hyperparameters, producing the forecasts on the validation set, and evaluating them. The best configuration is selected based on the error on a validation set. Only the best model is stored and used during inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast import NeuralForecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `val_size` parameter of the `fit` method to control the length of the validation set. In this case we set the validation set as twice the forecasting horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 8\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=[model], freq='M')\n",
    "nf.fit(df=Y_df, val_size=24)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of the hyperparameter tuning are available in the `results` attribute of the `Auto` model. Use the `get_dataframe` method to get the results in a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "            loss  time_this_iter_s   done  timesteps_total  episodes_total  \\\n",
       "0      21.173204          3.645993  False              NaN             NaN   \n",
       "1      33.843426          3.756614  False              NaN             NaN   \n",
       "2      17.750280          8.573898  False              NaN             NaN   \n",
       "3      24.573055          6.987517  False              NaN             NaN   \n",
       "4  474221.937500          4.912362  False              NaN             NaN   \n",
       "\n",
       "   training_iteration  trial_id                     experiment_id  \\\n",
       "0                   2  e20dbd9b  f62650f116914e18889bb96963c6b202   \n",
       "1                   2  75e09199  f62650f116914e18889bb96963c6b202   \n",
       "2                   2  0dc5925a  f62650f116914e18889bb96963c6b202   \n",
       "3                   2  352e03ff  f62650f116914e18889bb96963c6b202   \n",
       "4                   2  289bdd5e  f62650f116914e18889bb96963c6b202   \n",
       "\n",
       "                  date   timestamp  ...  config/input_size  \\\n",
       "0  2023-10-03_11-19-14  1696346354  ...                 24   \n",
       "1  2023-10-03_11-19-22  1696346362  ...                 24   \n",
       "2  2023-10-03_11-19-36  1696346376  ...                 24   \n",
       "3  2023-10-03_11-19-50  1696346390  ...                 24   \n",
       "4  2023-10-03_11-20-00  1696346400  ...                 24   \n",
       "\n",
       "   config/learning_rate config/loss config/max_steps  \\\n",
       "0              0.000415       MAE()              100   \n",
       "1              0.000068       MAE()              100   \n",
       "2              0.001615       MAE()              100   \n",
       "3              0.003405       MAE()              100   \n",
       "4              0.080117       MAE()              100   \n",
       "\n",
       "   config/n_freq_downsample  config/n_pool_kernel_size  config/random_seed  \\\n",
       "0              [168, 24, 1]                 [16, 8, 1]                   7   \n",
       "1               [24, 12, 1]                 [16, 8, 1]                   4   \n",
       "2                 [1, 1, 1]                  [2, 2, 2]                   8   \n",
       "3                 [1, 1, 1]                  [2, 2, 2]                   5   \n",
       "4              [168, 24, 1]                 [16, 8, 1]                   5   \n",
       "\n",
       "   config/val_check_steps  config/valid_loss  \\\n",
       "0                      50              MAE()   \n",
       "1                      50              MAE()   \n",
       "2                      50              MAE()   \n",
       "3                      50              MAE()   \n",
       "4                      50              MAE()   \n",
       "\n",
       "                                              logdir  \n",
       "0  /Users/cchallu/ray_results/_train_tune_2023-10...  \n",
       "1  /Users/cchallu/ray_results/_train_tune_2023-10...  \n",
       "2  /Users/cchallu/ray_results/_train_tune_2023-10...  \n",
       "3  /Users/cchallu/ray_results/_train_tune_2023-10...  \n",
       "4  /Users/cchallu/ray_results/_train_tune_2023-10...  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = nf.models[0].results.get_dataframe()\n",
    "results.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use the `predict` method to forecast the next 12 months using the optimal hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 113.97it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>AutoNHITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>442.346680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>439.409821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>477.709930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>503.884064</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>521.344421</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds   AutoNHITS\n",
       "0        1.0 1961-01-31  442.346680\n",
       "1        1.0 1961-02-28  439.409821\n",
       "2        1.0 1961-03-31  477.709930\n",
       "3        1.0 1961-04-30  503.884064\n",
       "4        1.0 1961-05-31  521.344421"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict()\n",
    "Y_hat_df = Y_hat_df.reset_index()\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. `Optuna` backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we show how to use the `Optuna` backend. `Optuna` is a lightweight and versatile platform for hyperparameter optimization. If you plan to use the `Tune` backend, you can skip this section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.a Define hyperparameter grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `Auto` model contains a default search space that was extensively tested on multiple large-scale datasets. Search spaces are specified with a function that returns a dictionary, where keys corresponds to the model's hyperparameter and the value is a `suggest` function to specify how the hyperparameter will be sampled. For example, use `suggest_int` to sample integers uniformly, and `suggest_categorical` to sample values of a list. See https://optuna.readthedocs.io/en/stable/reference/generated/optuna.trial.Trial.html for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.a.1 Default hyperparameter grid\n",
    "\n",
    "The default search space dictionary can be accessed through the `get_default_config` function of the `Auto` model. This is useful if you wish to use the default parameter configuration but want to change one or more hyperparameter spaces without changing the other default values.\n",
    "\n",
    "To extract the default config, you need to define:\n",
    "* `h`: forecasting horizon.\n",
    "* `backend`: backend to use.\n",
    "* `n_series`: Optional, the number of unique time series, required only for Multivariate models. \n",
    "\n",
    "In this example, we will use `h=12` and we use `optuna` as backend. We will use the default hyperparameter space but only change `random_seed` range and `n_pool_kernel_size`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING) # Use this to disable training prints from optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhits_default_config = AutoNHITS.get_default_config(h = 12, backend=\"optuna\")                   # Extract the default hyperparameter settings\n",
    "\n",
    "def config_nhits(trial):\n",
    "    config = {**nhits_default_config(trial)}\n",
    "    config.update({\n",
    "                   \"random_seed\": trial.suggest_int(\"random_seed\", 1, 10), \n",
    "                   \"n_pool_kernel_size\": trial.suggest_categorical(\"n_pool_kernel_size\", [[2, 2, 2], [16, 8, 1]])\n",
    "                   })\n",
    "    return config    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.a.2 Custom hyperparameter grid\n",
    "\n",
    "More generally, users can define fully customized search spaces tailored for particular datasets and tasks, by fully specifying a hyperparameter search space function.\n",
    "\n",
    "In the following example we are optimizing the `learning_rate` and two `NHITS` specific hyperparameters: `n_pool_kernel_size` and `n_freq_downsample`. Additionaly, we use the search space to modify default hyperparameters, such as `max_steps` and `val_check_steps`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def config_nhits(trial):\n",
    "    return {\n",
    "        \"max_steps\": 100,                                                                                               # Number of SGD steps\n",
    "        \"input_size\": 24,                                                                                               # Size of input window\n",
    "        \"learning_rate\": trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-1),                                         # Initial Learning rate\n",
    "        \"n_pool_kernel_size\": trial.suggest_categorical(\"n_pool_kernel_size\", [[2, 2, 2], [16, 8, 1]]),                 # MaxPool's Kernelsize\n",
    "        \"n_freq_downsample\": trial.suggest_categorical(\"n_freq_downsample\", [[168, 24, 1], [24, 12, 1], [1, 1, 1]]),    # Interpolation expressivity ratios\n",
    "        \"val_check_steps\": 50,                                                                                          # Compute validation every 50 steps\n",
    "        \"random_seed\": trial.suggest_int(\"random_seed\", 1, 10),                                                         # Random seed\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.b Instantiate `Auto` model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate an `Auto` model you need to define:\n",
    "\n",
    "* `h`: forecasting horizon.\n",
    "* `loss`: training and validation loss from `neuralforecast.losses.pytorch`.\n",
    "* `config`: hyperparameter search space. If `None`, the `Auto` class will use a pre-defined suggested hyperparameter space.\n",
    "* `search_alg`: search algorithm (from `optuna.samplers`), default is TPESampler (Tree-structured Parzen Estimator). Refer to https://optuna.readthedocs.io/en/stable/reference/samplers/index.html for more information on the different search algorithm options.\n",
    "* `backend`: backend to use, default is `ray`. If `optuna`, the `Auto` class will use the `Optuna` backend.\n",
    "* `num_samples`: number of configurations explored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoNHITS(h=12,\n",
    "                  loss=MAE(),\n",
    "                  config=config_nhits,\n",
    "                  search_alg=optuna.samplers.TPESampler(),\n",
    "                  backend='optuna',\n",
    "                  num_samples=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Configuration dictionaries and search algorithms for `Tune` and `Optuna` are not interchangeable! Use the appropriate type of search algorithm and custom configuration dictionary for each backend.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.c Train model and predict with `Core` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `val_size` parameter of the `fit` method to control the length of the validation set. In this case we set the validation set as twice the forecasting horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 6\n",
      "Global seed set to 6\n",
      "Global seed set to 1\n",
      "Global seed set to 1\n",
      "Global seed set to 7\n",
      "Global seed set to 4\n",
      "Global seed set to 9\n",
      "Global seed set to 8\n",
      "Global seed set to 7\n",
      "Global seed set to 7\n",
      "Global seed set to 6\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=[model], freq='M')\n",
    "nf.fit(df=Y_df, val_size=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results of the hyperparameter tuning are available in the `results` attribute of the `Auto` model. Use the `trials_dataframe` method to get the results in a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>value</th>\n",
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       "      <th>datetime_complete</th>\n",
       "      <th>duration</th>\n",
       "      <th>params_learning_rate</th>\n",
       "      <th>params_n_freq_downsample</th>\n",
       "      <th>params_n_pool_kernel_size</th>\n",
       "      <th>params_random_seed</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.964735e+01</td>\n",
       "      <td>2023-10-23 19:13:30.251719</td>\n",
       "      <td>2023-10-23 19:13:33.007086</td>\n",
       "      <td>0 days 00:00:02.755367</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>[24, 12, 1]</td>\n",
       "      <td>[2, 2, 2]</td>\n",
       "      <td>2</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.790444e+03</td>\n",
       "      <td>2023-10-23 19:13:33.007483</td>\n",
       "      <td>2023-10-23 19:13:35.823089</td>\n",
       "      <td>0 days 00:00:02.815606</td>\n",
       "      <td>0.026500</td>\n",
       "      <td>[24, 12, 1]</td>\n",
       "      <td>[2, 2, 2]</td>\n",
       "      <td>10</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.193000e+01</td>\n",
       "      <td>2023-10-23 19:13:35.823607</td>\n",
       "      <td>2023-10-23 19:13:38.599414</td>\n",
       "      <td>0 days 00:00:02.775807</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>[168, 24, 1]</td>\n",
       "      <td>[2, 2, 2]</td>\n",
       "      <td>7</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.147799e+08</td>\n",
       "      <td>2023-10-23 19:13:38.600149</td>\n",
       "      <td>2023-10-23 19:13:41.440307</td>\n",
       "      <td>0 days 00:00:02.840158</td>\n",
       "      <td>0.059274</td>\n",
       "      <td>[1, 1, 1]</td>\n",
       "      <td>[16, 8, 1]</td>\n",
       "      <td>5</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.140740e+01</td>\n",
       "      <td>2023-10-23 19:13:41.440833</td>\n",
       "      <td>2023-10-23 19:13:44.184860</td>\n",
       "      <td>0 days 00:00:02.744027</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>[168, 24, 1]</td>\n",
       "      <td>[16, 8, 1]</td>\n",
       "      <td>5</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.606544e+01</td>\n",
       "      <td>2023-10-23 19:13:44.185291</td>\n",
       "      <td>2023-10-23 19:13:46.945672</td>\n",
       "      <td>0 days 00:00:02.760381</td>\n",
       "      <td>0.005477</td>\n",
       "      <td>[1, 1, 1]</td>\n",
       "      <td>[16, 8, 1]</td>\n",
       "      <td>8</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.301640e+04</td>\n",
       "      <td>2023-10-23 19:13:46.946108</td>\n",
       "      <td>2023-10-23 19:13:49.805633</td>\n",
       "      <td>0 days 00:00:02.859525</td>\n",
       "      <td>0.056746</td>\n",
       "      <td>[1, 1, 1]</td>\n",
       "      <td>[16, 8, 1]</td>\n",
       "      <td>3</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>4.972713e+01</td>\n",
       "      <td>2023-10-23 19:13:49.806278</td>\n",
       "      <td>2023-10-23 19:13:52.577180</td>\n",
       "      <td>0 days 00:00:02.770902</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>[24, 12, 1]</td>\n",
       "      <td>[2, 2, 2]</td>\n",
       "      <td>9</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2.138879e+01</td>\n",
       "      <td>2023-10-23 19:13:52.577678</td>\n",
       "      <td>2023-10-23 19:13:55.372792</td>\n",
       "      <td>0 days 00:00:02.795114</td>\n",
       "      <td>0.007136</td>\n",
       "      <td>[1, 1, 1]</td>\n",
       "      <td>[2, 2, 2]</td>\n",
       "      <td>9</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2.094145e+01</td>\n",
       "      <td>2023-10-23 19:13:55.373149</td>\n",
       "      <td>2023-10-23 19:13:58.125058</td>\n",
       "      <td>0 days 00:00:02.751909</td>\n",
       "      <td>0.004655</td>\n",
       "      <td>[1, 1, 1]</td>\n",
       "      <td>[2, 2, 2]</td>\n",
       "      <td>6</td>\n",
       "      <td>COMPLETE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number         value             datetime_start          datetime_complete  \\\n",
       "0       0  2.964735e+01 2023-10-23 19:13:30.251719 2023-10-23 19:13:33.007086   \n",
       "1       1  2.790444e+03 2023-10-23 19:13:33.007483 2023-10-23 19:13:35.823089   \n",
       "2       2  2.193000e+01 2023-10-23 19:13:35.823607 2023-10-23 19:13:38.599414   \n",
       "3       3  1.147799e+08 2023-10-23 19:13:38.600149 2023-10-23 19:13:41.440307   \n",
       "4       4  2.140740e+01 2023-10-23 19:13:41.440833 2023-10-23 19:13:44.184860   \n",
       "5       5  1.606544e+01 2023-10-23 19:13:44.185291 2023-10-23 19:13:46.945672   \n",
       "6       6  1.301640e+04 2023-10-23 19:13:46.946108 2023-10-23 19:13:49.805633   \n",
       "7       7  4.972713e+01 2023-10-23 19:13:49.806278 2023-10-23 19:13:52.577180   \n",
       "8       8  2.138879e+01 2023-10-23 19:13:52.577678 2023-10-23 19:13:55.372792   \n",
       "9       9  2.094145e+01 2023-10-23 19:13:55.373149 2023-10-23 19:13:58.125058   \n",
       "\n",
       "                duration  params_learning_rate params_n_freq_downsample  \\\n",
       "0 0 days 00:00:02.755367              0.000074              [24, 12, 1]   \n",
       "1 0 days 00:00:02.815606              0.026500              [24, 12, 1]   \n",
       "2 0 days 00:00:02.775807              0.000337             [168, 24, 1]   \n",
       "3 0 days 00:00:02.840158              0.059274                [1, 1, 1]   \n",
       "4 0 days 00:00:02.744027              0.000840             [168, 24, 1]   \n",
       "5 0 days 00:00:02.760381              0.005477                [1, 1, 1]   \n",
       "6 0 days 00:00:02.859525              0.056746                [1, 1, 1]   \n",
       "7 0 days 00:00:02.770902              0.000021              [24, 12, 1]   \n",
       "8 0 days 00:00:02.795114              0.007136                [1, 1, 1]   \n",
       "9 0 days 00:00:02.751909              0.004655                [1, 1, 1]   \n",
       "\n",
       "  params_n_pool_kernel_size  params_random_seed     state  \n",
       "0                 [2, 2, 2]                   2  COMPLETE  \n",
       "1                 [2, 2, 2]                  10  COMPLETE  \n",
       "2                 [2, 2, 2]                   7  COMPLETE  \n",
       "3                [16, 8, 1]                   5  COMPLETE  \n",
       "4                [16, 8, 1]                   5  COMPLETE  \n",
       "5                [16, 8, 1]                   8  COMPLETE  \n",
       "6                [16, 8, 1]                   3  COMPLETE  \n",
       "7                 [2, 2, 2]                   9  COMPLETE  \n",
       "8                 [2, 2, 2]                   9  COMPLETE  \n",
       "9                 [2, 2, 2]                   6  COMPLETE  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = nf.models[0].results.trials_dataframe()\n",
    "results.drop(columns='user_attrs_ALL_PARAMS')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we use the `predict` method to forecast the next 12 months using the optimal hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 112.75it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>AutoNHITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-01-31</td>\n",
       "      <td>445.272858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-02-28</td>\n",
       "      <td>469.633423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-03-31</td>\n",
       "      <td>475.265289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-04-30</td>\n",
       "      <td>483.228516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1961-05-31</td>\n",
       "      <td>516.583496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds   AutoNHITS\n",
       "0        1.0 1961-01-31  445.272858\n",
       "1        1.0 1961-02-28  469.633423\n",
       "2        1.0 1961-03-31  475.265289\n",
       "3        1.0 1961-04-30  483.228516\n",
       "4        1.0 1961-05-31  516.583496"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df_optuna = nf.predict()\n",
    "Y_hat_df_optuna = Y_hat_df_optuna.reset_index()\n",
    "Y_hat_df_optuna.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we compare the forecasts produced by the `AutoNHITS` model with both backends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
    "plot_df = pd.concat([Y_df, Y_hat_df]).reset_index()\n",
    "\n",
    "plt.plot(plot_df['ds'], plot_df['y'], label='y')\n",
    "plt.plot(plot_df['ds'], plot_df['AutoNHITS'], label='Ray')\n",
    "plt.plot(Y_hat_df_optuna['ds'], Y_hat_df_optuna['AutoNHITS'], label='Optuna')\n",
    "\n",
    "ax.set_title('AirPassengers Forecast', fontsize=22)\n",
    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
    "ax.legend(prop={'size': 15})\n",
    "ax.grid()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)\n",
    "- [James Bergstra, Remi Bardenet, Yoshua Bengio, and Balazs Kegl (2011). \"Algorithms for Hyper-Parameter Optimization\". In: Advances in Neural Information Processing Systems. url: https://proceedings.neurips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf](https://proceedings.neurips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf)\n",
    "- [Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabas Poczos, Eric P. Xing (2019). \"Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly\". Journal of Machine Learning Research. url: https://arxiv.org/abs/1903.06694](https://arxiv.org/abs/1903.06694)\n",
    "- [Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar (2016). \"Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization\". Journal of Machine Learning Research. url: https://arxiv.org/abs/1603.06560](https://arxiv.org/abs/1603.06560)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
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 "nbformat_minor": 2
}
